A high-level semiotic trust agent scoring model for collaborative virtual organisations

In this paper, we describe how a semiotic ladder, together with a supportive trust agent, can be used to address “soft” trust issues in the context of collaborative Virtual Organisations (VO). The intention is to offer all parties better support for trust (as reputation) management including the reduction of risk and improved reliability of VO e-services. The semiotic ladder is intended to support the VO e-service lifecycle through the articulation of e-trust at various levels of system abstraction, including trust as measurable confidence. At the social level, reputation and reliability measures of e-trust are the relevant dimensions as regards choice of VO partner and are also relevant to the negotiation of service level agreements between the VO partners. By contrast, at the lower levels of the trust ladder, e-trust measures typically address the degree to which secure sign on and message level security conforms to various tangible technological security protocols. The novel trust agent provides the e-service consumer with an objective measure of the trustworthiness of the e-service at run-time, just prior to its actual consumption. Specifically, VO e-service consumer confidence level is informed, by leveraging third party objective evidence. This evidence comprises a set of Corporate Governance (CG) scores. These scores are used as a trust proxy for the "real" owner of the VO. There are also inherent limitations associated with the use of CG scores. These are duly acknowledged

Theoretical and Computational Basis for CATNETS - Annual Report Year 2

In this work the self-organising potential of the CATNETS allocation mechanism is described to provide a more comprehensive view on the research done in this project. The formal description of either the centralised and decentralised approach is presented. Furthermore the agents' bidding model is described and a comprehensive overview on how the catallactic mechanism is incorporated into the middleware and simulator environments is given. --Decentralized Market Mechanisms,Centralized Market Mechanisms,Catallaxy,Market Engineering,Simulator Integration,Prototype Integration

A Self-adaptive Agent-based System for Cloud Platforms

Cloud computing is a model for enabling on-demand network access to a shared pool of computing resources, that can be dynamically allocated and released with minimal effort. However, this task can be complex in highly dynamic environments with various resources to allocate for an increasing number of different users requirements. In this work, we propose a Cloud architecture based on a multi-agent system exhibiting a self-adaptive behavior to address the dynamic resource allocation. This self-adaptive system follows a MAPE-K approach to reason and act, according to QoS, Cloud service information, and propagated run-time information, to detect QoS degradation and make better resource allocation decisions. We validate our proposed Cloud architecture by simulation. Results show that it can properly allocate resources to reduce energy consumption, while satisfying the users demanded QoS

An Approach to Ad hoc Cloud Computing

We consider how underused computing resources within an enterprise may be harnessed to improve utilization and create an elastic computing infrastructure. Most current cloud provision involves a data center model, in which clusters of machines are dedicated to running cloud infrastructure software. We propose an additional model, the ad hoc cloud, in which infrastructure software is distributed over resources harvested from machines already in existence within an enterprise. In contrast to the data center cloud model, resource levels are not established a priori, nor are resources dedicated exclusively to the cloud while in use. A participating machine is not dedicated to the cloud, but has some other primary purpose such as running interactive processes for a particular user. We outline the major implementation challenges and one approach to tackling them

A Generic Agent Organisation Framework For Autonomic Systems

Autonomic computing is being advocated as a tool for managing large, complex computing systems. Specifically, self-organisation provides a suitable approach for developing such autonomic systems by incorporating self-management and adaptation properties into large-scale distributed systems. To aid in this development, this paper details a generic problem-solving agent organisation framework that can act as a modelling and simulation platform for autonomic systems. Our framework describes a set of service-providing agents accomplishing tasks through social interactions in dynamically changing organisations. We particularly focus on the organisational structure as it can be used as the basis for the design, development and evaluation of generic algorithms for self-organisation and other approaches towards autonomic systems

Incorporating prediction models in the SelfLet framework: a plugin approach

A complex pervasive system is typically composed of many cooperating \emph{nodes}, running on machines with different capabilities, and pervasively distributed across the environment. These systems pose several new challenges such as the need for the nodes to manage autonomously and dynamically in order to adapt to changes detected in the environment. To address the above issue, a number of autonomic frameworks has been proposed. These usually offer either predefined self-management policies or programmatic mechanisms for creating new policies at design time. From a more theoretical perspective, some works propose the adoption of prediction models as a way to anticipate the evolution of the system and to make timely decisions. In this context, our aim is to experiment with the integration of prediction models within a specific autonomic framework in order to assess the feasibility of such integration in a setting where the characteristics of dynamicity, decentralization, and cooperation among nodes are important. We extend an existing infrastructure called \emph{SelfLets} in order to make it ready to host various prediction models that can be dynamically plugged and unplugged in the various component nodes, thus enabling a wide range of predictions to be performed. Also, we show in a simple example how the system works when adopting a specific prediction model from the literature